Barplots & ggplot2 Basics

class: center, middle, inverse, title-slide

.title[
# Barplots & <code>ggplot2</code> Basics
]
.author[
### Justin Post
]

---

layout: true

---

# Summarizing Categorical Variables

Categorical (Qualitative) variable - entries are a label or attribute

- Numerical summary: contingency tables
- Graphical summary: bar plots

---

# Summarizing Categorical Variables

Categorical (Qualitative) variable - entries are a label or attribute

- Numerical summary: contingency tables
- Graphical summary: bar plots

We can easily create plots in R (one of its real strengths!)

There are three major systems for plotting in R (and others that have been ported in)

-   Base R (built-in functions)
-   `lattice`
-   `ggplot2` (part of the tidyverse but not exactly)

We'll use `ggplot2` as it is very popular. There is a great [ggplot2 reference book here!](https://bookdown.org/rdpeng/exdata/plotting-systems.html)

---

# Reading in Our Data

First, let's read in the appendicitis data from the previous lecture.

```r
library(tidyverse)
library(readxl)
app_data <- read_excel("data/app_data.xlsx", sheet = 1)
app_data <- app_data |>
  mutate(BMI = as.numeric(BMI),
         US_Number = as.character(US_Number),
         SexF = factor(Sex, levels = c("female", "male"), labels = c("Female", "Male")),
         DiagnosisF = as.factor(Diagnosis),
         SeverityF = as.factor(Severity))
app_data
```

```
## # A tibble: 782 x 61
##     Age   BMI Sex    Height Weight Length_of_Stay Management   Severity     
##   <dbl> <dbl> <chr>   <dbl>  <dbl>          <dbl> <chr>        <chr>        
## 1  12.7  16.9 female    148   37                3 conservative uncomplicated
## 2  14.1  31.9 male      147   69.5              2 conservative uncomplicated
## 3  14.1  23.3 female    163   62                4 conservative uncomplicated
## 4  16.4  20.6 female    165   56                3 conservative uncomplicated
## 5  11.1  16.9 female    163   45                3 conservative uncomplicated
## # i 777 more rows
## # i 53 more variables: Diagnosis_Presumptive <chr>, Diagnosis <chr>,
## #   Alvarado_Score <dbl>, Paedriatic_Appendicitis_Score <dbl>,
## #   Appendix_on_US <chr>, Appendix_Diameter <dbl>, Migratory_Pain <chr>,
## #   Lower_Right_Abd_Pain <chr>, Contralateral_Rebound_Tenderness <chr>,
## #   Coughing_Pain <chr>, Nausea <chr>, Loss_of_Appetite <chr>,
## #   Body_Temperature <dbl>, WBC_Count <dbl>, Neutrophil_Percentage <dbl>, ...
```

---

# `ggplot2` Basics

[`ggplot` cheat sheet](https://rstudio.github.io/cheatsheets/data-visualization.pdf) is handy!

To get started:

-   `ggplot(data = data_frame)` creates a plot instance

---

# `ggplot2` Basics

[`ggplot` cheat sheet](https://rstudio.github.io/cheatsheets/data-visualization.pdf) is handy!

To get started:

-   `ggplot(data = data_frame)` creates a plot instance
-   Then we add "layers" to the plot (`geom` or `stat` layers)

-   This actually creates a visualization of the data
    
---

# `ggplot2` Basics

[`ggplot` cheat sheet](https://rstudio.github.io/cheatsheets/data-visualization.pdf) is handy!

To get started:

-   `ggplot(data = data_frame)` creates a plot instance
-   Then we add "layers" to the plot (`geom` or `stat` layers)

-   This actually creates a visualization of the data

-   Modify layer "mapping" args (usually with `aes()`)

-   Map variables to attributes of the plot
    -   Ex: size, color, x variable, y variable
    
---

# `ggplot2` Basics

[`ggplot` cheat sheet](https://rstudio.github.io/cheatsheets/data-visualization.pdf) is handy!

To get started:

-   `ggplot(data = data_frame)` creates a plot instance
-   Then we add "layers" to the plot (`geom` or `stat` layers)

-   This actually creates a visualization of the data

-   Modify layer "mapping" args (usually with `aes()`)

-   Map variables to attributes of the plot
    -   Ex: size, color, x variable, y variable

-   Improve by adding title layers, faceting, etc.

---

# `ggplot2` Barplots

-   Barplots via `ggplot() + geom_bar()`

-   Across x-axis we want our categories - specify with `aes(x = ...)`

```r
ggplot(data = app_data |> drop_na(SexF), aes(x = SexF))
```

---

# `ggplot2` Barplots

-   Barplots via `ggplot() + geom_bar()`

-   Must add geom (or stat) layer!

```r
ggplot(data = app_data |> drop_na(SexF), aes(x = SexF)) + 
  geom_bar()
```

---

# `ggplot2` Barplots

-   Generally: Save base object with **global** `aes()` assignments, then add layers

```r
g <- ggplot(data = app_data |> drop_na(SexF), aes(x = SexF))
g + geom_bar()
```

---

# Better Labeling Needed

- We can easily improve the labeling on the x-axis variable (see the 2nd page of cheat sheet on the right!)

```r
g <- ggplot(data = app_data |> drop_na(SexF), aes(x = SexF))
g + geom_bar() +
    labs(x = "Sex")
```

---

# `aes()` Arguments

-   `aes()` defines visual properties of objects in the plot

-   Map variables in the data frame to plot elements

`x = , y = , size = , shape = , color = , alpha = , ...`

- For a bar plot, from the cheat sheet we see

`d + geom_bar()`

`x, alpha, color, fill, linetype, size, weight`

---

# `aes()` Arguments for Barplots

-   **Stacked barplot** created by via `fill` aesthetic

-   Automatic assignment of colors and creation of legends for `aes` elements (except group)

```r
g <- ggplot(data = app_data |> drop_na(SexF, DiagnosisF), aes(x = SexF, fill = DiagnosisF))
g + geom_bar()+
    labs(x = "Sex")
```

---

# Update Legend

- Can change automatically created labels/legend

```r
g <- ggplot(data = app_data |> drop_na(SexF, DiagnosisF), aes(x = SexF, fill = DiagnosisF))
g + geom_bar() + 
  labs(x = "Sex")+ 
  scale_fill_discrete("Diagnosis")
```

---

# `ggplot2` Global vs Local Aesthetics

`data` and `aes` can be set in two ways;

-   'globally' (for all layers) via the `aes()` function in the `ggplot()` call

-   'locally' (for just that layer) via the `geom` or `stat` layer's `aes()`

---

# `ggplot2` Global vs Local Aesthetics

`data` and `aes` can be set in two ways;

-   'globally' (for all layers) via the `aes()` function in the `ggplot()` call

-   'locally' (for just that layer) via the `geom` or `stat` layer's `aes()`

```r
#global 
ggplot(data = app_data |> drop_na(SexF, DiagnosisF), aes(x = SexF, fill = DiagnosisF)) +
  geom_bar()
```

```r
#some local, some global
ggplot(data = app_data |> drop_na(SexF, DiagnosisF), aes(x = SexF)) +
  geom_bar(aes(fill = DiagnosisF))
```

```r
#all local
ggplot(data = app_data |> drop_na(SexF, DiagnosisF)) +
  geom_bar(aes(x = SexF, fill = DiagnosisF))
```

---

# `ggplot2` Horizontal Barplots

-   Easy to rotate a plot with `coord_flip()`

```r
ggplot(data = app_data |> drop_na(SexF, DiagnosisF), aes(x = SexF, fill = DiagnosisF)) + 
  geom_bar() + 
  labs(x = "Sex")+ 
  scale_fill_discrete("Diagnosis")+
  coord_flip()
```

---

# `ggplot2` Stat vs Geom layers

Note: Most `geom`s have a corresponding `stat` layer that can be used

```
geom_bar(mapping = NULL, data = NULL, stat = "count",
         position = "stack", ..., width = NULL, binwidth = NULL, na.rm = FALSE,
         show.legend = NA, inherit.aes = TRUE)
```

---

# `ggplot2` Stat vs Geom layers

Note: Most `geom`s have a corresponding `stat` layer that can be used

```
geom_bar(mapping = NULL, data = NULL, stat = "count",
         position = "stack", ..., width = NULL, binwidth = NULL, na.rm = FALSE,
         show.legend = NA, inherit.aes = TRUE)
```
-   Equivalent plots via:

```r
ggplot(data = app_data |> drop_na(SexF, DiagnosisF), aes(x = SexF, fill = DiagnosisF)) + geom_bar()
ggplot(data = app_data |> drop_na(SexF, DiagnosisF), aes(x = SexF, fill = DiagnosisF)) + stat_count()
```

---

# `ggplot2` Stat vs Geom layers

-   Can modify the stat: if you have summary data, specify `y` and use `stat = identity`

```r
ggplot(data = app_data |> 
         drop_na(SexF, DiagnosisF) |>
         group_by(SexF, DiagnosisF) |>
         summarize(count = n()), aes(x = SexF, y = count, fill = DiagnosisF)) +
  geom_bar(stat = "identity")
```

---

# `ggplot2` Side-By-Side Barplots

-   **Side-by-side barplot** created via the `position` aesthetic

-   `dodge` for side-by-side bar plot
    -   `jitter` for continuous data with many points at same values
    -   `fill` stacks bars and standardises each stack to have constant height
    -   `stack` stacks bars on top of each other

---

# `ggplot2` Side-By-Side Barplots

-   **Side-by-side barplot** created by via `position` aesthetic

```r
ggplot(data = app_data |> drop_na(SexF, DiagnosisF), aes(x = SexF, fill = DiagnosisF)) + 
  geom_bar(position = "dodge") + 
  labs(x = "Sex")+ 
  scale_fill_discrete("Diagnosis") 
```

---

# `ggplot2` Filled Barplots

-   `position = fill` stacks bars and standardizes each stack to have constant height (especially useful with equal group sizes)

```r
ggplot(data = app_data |> drop_na(SexF, DiagnosisF), aes(x = SexF, fill = DiagnosisF)) + 
  geom_bar(position = "fill") + 
  labs(x = "Sex")+ 
  scale_fill_discrete("Diagnosis") 
```

---

# Recap!

- How to summarize categorical data?

- Numerically?  
    + Tables (contingency tables)  
        * Show frequency of categories

- Graphically?
    + Barplots

- ggplot (create object, add layers)
    + Data Frame
    + Geoms (Vis type)
    + Aesthetic (aes) 
    + Coordinate system, stat, labels, etc.